IMO, the 65-70% of responsibilities and skill sets required for BI Engineer, Data Scientist, and Data Analyst overlap. To become successful in any of the above roles, you will need to know at least the following skills.
1. Business knowledge: You should have sound business knowledge of the company you are analyzing the data. You need to know how to leverage data to make a business profitable by improving the decision-making process faster and simpler. You should know the key performance indicators (KPI) of the business and how they will impact the short-term and long-term goals of the company.
2. Structured Query Language (SQL): SQL is a standardized programming language that is used to manage relational databases and perform various operations on the data in them.
The most common use of SQL for BI Engineer/Data Scientist/Data Analyst is to retrieve the required data from various tables/views ( Equi/Left/Right/Outer joins) and perform data cleanup/ transformations (various string functions), aggregations (Sum, Average, Stddev, etc.), Extreme Value ( Min/Max), Grouping (Group By, Having clause).
3. Data Visualization: The critical part of the job is to identify the audience and develop visualizations. If your audience is upper management, you will need to develop a report/dashboard that will provide a high-level view of corporate performance to monitor the business and take action based on insights obtained through business data. You should know how to use various charts to visualize the data.
If your audience is Business Analysts, you will need to develop a visualization that will provide a drill-down capability that will allow more detailed views of a specific KPI.
4. Programming language: SQL is the most widely used language by BI Engineer/Data Scientist/Data Analyst but when it comes to cleaning, manipulating, analyzing, and visualizing data, you will be using either Python or R. Both Python and R are easy-to-learn open-source free languages run on Microsoft Windows, Linux, and macOS platforms.
Python is a high-level, general-purpose programming language, and its syntax mimics natural language. Python code is used for a wide variety of tasks including Data science and data analysis as well as scripting to automate tasks.
R is a software environment and statistical programming language built for statistical computing and data visualization. R is mostly used for Manipulating data, Statistical analysis, and Visualizing data.
Data Security: Ensuring that users with requisite permissions have access to view and interact with data. There are two types of data securities that allow authorized access to data.
Object Level Security – Secures objects like Tables and Columns. From an end-user perspective, the table or column simply doesn’t exist. Row-level Security – Limits the data an end-user has visibility within the same report based on his/her role in the organization, location, etc.